What your agents are actually doing
Publishing an agent is the start, not the finish. Analytics tell you how much work it is absorbing, which questions defeat it, and what each answer costs - the three numbers that decide what you change next.
Demand, in your customers' words
The questions people actually type are better product research than any survey, and they arrive for free.
Gaps ranked by frequency
Unanswered questions are sorted by how often they come up, so the next page you write is the one that pays the most.
Per agent, not just per account
Break volume, outcomes and cost down by agent to see which ones deserve more of your attention.
The same meter that bills you
Usage here is the usage you pay for - one number, not an estimate that drifts from the invoice.
What teams build with it
Three jobs this does the day you turn it on.
Find the questions you never answered
The recurring questions your agent could not resolve are your content gaps, ranked by how often real customers hit them.
Prove it is working
Conversations handled, contacts produced and questions resolved make the case for the agent in numbers rather than anecdotes.
See what it costs
Usage is metered per agent, so you can tell which of your agents earns its keep and which one is answering the same question a thousand times.
Where you set this up
The same build screen every feature is configured from — this row is the one you would touch.
Under the hood
The details you will want before you commit.
How it works
Publish
Metrics start collecting from the first conversation, with nothing to instrument.
Read the gaps
Work down the unanswered questions and add what is missing to your knowledge.
Watch it improve
The same questions stop appearing, and the resolved share climbs.
Where it earns its keep
Industry playbooks that lean on this piece — what to build first, and what to charge.
Frequently asked questions
Unanswered questions. Volume tells you the agent is being used; the questions it could not answer tell you exactly what to fix next, and each fix compounds.
Yes. Everything is broken down per agent, which is what makes it possible to tell a workhorse from an experiment.
Conversations link to contact records so you can follow one customer's history. The analytics themselves are about patterns across conversations, not surveillance of individuals.
The same metering that bills you is what you see here, so the usage figures and the invoice never disagree.
How it compares
Side by side with the tools people usually weigh this against.
SuperCognit vs Chatbase →
Chatbase is a popular no-code builder for chatbots trained on your data, aimed mostly at website support.
SuperCognit vs Botpress →
Botpress is a developer-oriented platform for building conversational AI with a visual flow editor.
SuperCognit vs Zendesk AI →
Zendesk AI adds AI agents and copilots to Zendesk's enterprise customer-service suite.